'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn

import torchvision
import torchvision.transforms as transforms

import os
import argparse

from models import *
from utils import progress_bar

os.environ["CUDA_VISIBLE_DEVICES"] = '1'
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--batch_size', default=256, type=int, help='batch size of image')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
parser.add_argument('--test', action='store_true', help='test from checkpoint')
parser.add_argument('--use_cg', action='store_true', help='test from checkpoint')
args = parser.parse_args()

device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0  # best test accuracy
start_epoch = 0  # start from epoch 0 or last checkpoint epoch

# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

trainset = torchvision.datasets.CIFAR10(
    root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(
    trainset, batch_size=args.batch_size, shuffle=True, num_workers=16)

testset = torchvision.datasets.CIFAR10(
    root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(
    testset, batch_size=args.batch_size, shuffle=False, num_workers=16)

classes = ('plane', 'car', 'bird', 'cat', 'deer',
           'dog', 'frog', 'horse', 'ship', 'truck')

# Model
print('==> Building model..')
net = VGG_CG('VGG11')
# net = ResNet18()
# net = PreActResNet18()
# net = GoogLeNet()
# net = DenseNet121()
# net = ResNeXt29_2x64d()
# net = MobileNet()
# net = MobileNetV2()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
# net = ShuffleNetV2(1)
# net = EfficientNetB0()
# net = RegNetX_200MF()
# net = SimpleDLA()
net = net.to(device)
# if device == 'cuda':
#     net = torch.nn.DataParallel(net)
#     cudnn.benchmark = True

if args.resume or args.test:
    # Load checkpoint.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
    checkpoint = torch.load('./checkpoint/ckpt.pth')
    net.load_state_dict(checkpoint['net'])
    best_acc = checkpoint['acc']
    start_epoch = checkpoint['epoch']

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=args.lr * args.batch_size / 128, momentum=0.9, weight_decay=5e-4)
div = 100 // 5
scheduler = torch.optim.lr_scheduler.MultiStepLR(
                                    optimizer, 
                                    milestones=[div*3, div*4], 
                                    gamma=0.1,
                                    last_epoch=-1)

num_out = []
num_full = []


# Training
def train(epoch):
    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets)
        #########
        if epoch > 30:
            for name, param in net.named_parameters():
                if 'threshold' in name:
                    loss += 0.000001 * torch.sum((param) ** 2)
        #########
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                     % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))


    num_out = []
    num_full = []
    def _report_sparsity(m):
        if isinstance(m, CGConv2d):
            num_out.append(m.num_out)
            num_full.append(m.num_full)
    
    if args.use_cg:
        net.apply(_report_sparsity)
        print(num_out)
        print(num_full)
        print('Sparsity of the update phase: %.1f %%' % (100-np.sum(num_full)*1.0/np.sum(num_out)*100))
        # num_out = []
        # num_full = []



def test(epoch):
    global best_acc
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            if batch_idx == 0:
                print(inputs.shape)
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = net(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

            progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                         % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))

    # Save checkpoint.
    acc = 100.*correct/total
    if acc > best_acc:
        print('Saving..')
        state = {
            'net': net.state_dict(),
            'acc': acc,
            'epoch': epoch,
        }
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(state, './checkpoint/ckpt.pth')
        best_acc = acc

    num_out = []
    num_full = []
    def _report_sparsity(m):
        if isinstance(m, CGConv2d):
            num_out.append(m.num_out)
            num_full.append(m.num_full)
    if args.use_cg:
        net.apply(_report_sparsity)
        print(num_out)
        print(num_full)
        print('Sparsity of the update phase: %.1f %%' % (100-np.sum(num_full)*1.0/np.sum(num_out)*100))
        # num_out = []
        # num_full = []


if args.test:
    test(0)
else:
    for epoch in range(start_epoch, start_epoch+50):
        train(epoch)
        test(epoch)
        scheduler.step()
